Method, apparatus, and computer program product for determining if probe data points have been map-matched

ABSTRACT

A method, apparatus, and computer program product are provided for determining whether probe data points are map-matched or non-map-matched such that the probe data can be processed and analyzed appropriately without introducing bias into the analysis which may be caused by map-matching ahead of analysis. A mapping system includes a memory having map data stored therein, and processing circuitry. The processing circuitry may be configured to receive probe data points associated with a plurality of vehicles. Each probe data point is received from a probe apparatus of a plurality of probe apparatuses. The probe apparatus includes a plurality of sensors and being onboard a respective vehicle. Each probe data point includes location information associated with the respective probe apparatus.

TECHNOLOGICAL FIELD

An example embodiment of the present invention relates to determiningwhether probe data points have been map-matched or if they are inraw-data form, and more particularly, to analyzing a plurality of probedata points to establish those that are map-matched and those that arenot.

BACKGROUND

Service providers and device manufacturers (e.g., wireless, cellular,navigation, etc.) are continually challenged to deliver value andconvenience to consumers by providing compelling and useful services.Location-based services have been developed to provide users with usefuland relevant information regarding route planning and to facilitateroute guidance along the way. Substantially static data regardingroadways is used in conjunction with dynamic data, such as traffic,construction, and incident information to provide accurate and timelyinformation to a driver to help route planning and decision-making.

Data received from infrastructure monitoring systems and crowd-sourceddata has become ubiquitous and may be available for facilitating routeguidance and navigation system information. However, this data can bemined to provide various other services to users and to grow theavailability of location-based services.

BRIEF SUMMARY

A method, apparatus, and computer program product are provided inaccordance with an example embodiment described herein for determiningwhether probe data points are map-matched or non-map-matched such thatthe probe data can be processed and analyzed appropriately withoutintroducing bias into the analysis which may be caused by map-matchingahead of analysis. An example embodiment may provide a mapping systemincluding a memory having map data stored therein, and processingcircuitry. The processing circuitry may be configured to receive probedata points associated with a plurality of vehicles, each probe datapoint received from a probe apparatus of a plurality of probeapparatuses, each probe apparatus including a one or more sensors andbeing onboard a respective vehicle, where each probe data point includeslocation information associated with the respective probe apparatus. Theprocessing circuitry may further be configured to: determine a locationand a road segment corresponding to the location of each of the probedata points; generate, from the probe data points associated with afirst road segment, a probe density histogram for the first roadsegment, where the probe density histogram represents a volume of probedata points at each of a plurality of positions across a width of theroad segment; identify, based on the probe density histogram, probe datapoints that are determined to be map-matched and classify the probe datapoints determined to be map-matched as map-matched probe data points;identify, based on the probe density histogram, probe data points thatare determined to be non-map-matched and classify the probe data pointsdetermined to be non-map-matched as non-map-matched probe data points.The probe data points classified as map-matched may be analyzed withrespect to a position of a road segment of a digital map correspondingto the map-matched road segment. The probe data points classified asnon-map-matched may be analyzed to establish one or more of laneposition, lane-based traffic flow, or road centerline accuracy.

The probe data points associated with a plurality of vehicles may bereceived from a probe data provider, where the probe data provider maybe classified according to whether the probe data provided is classifiedas map-matched, as non-map-matched, or as a combination of map-matchedand non-map-matched. In response to the probe data provider beingclassified as providing a combination of map-matched and non-map-matchedprobe data, the processing circuitry may be configured to determine aproportion of the probe data points classified as map-matched, and aproportion of the probe data points classified as non-map-matched, andto classify the probe data provider according to the proportionsdetermined. The probe data points may be grouped into bins at each ofthe plurality of positions across the width of the road segment. Theprocessing circuitry configured to identify probe data points that aredetermined to be map-matched may include processing circuitry configuredto: identify a relatively high proportion of probe data points groupedto at least one of the plurality of positions across the width of theroad segment, where the relatively high proportion of probe data pointsis established based on a statistically significant deviation at the atleast one of the plurality of positions across the width of the roadsegment relative to the probe density histogram adjacent the at leastone of the plurality of positions; and identify the probe data pointsgrouped to the at least one of the plurality of positions across thewidth of the road segment as map-matched and classify the probe datapoints grouped to the at least one of the plurality of positions asmap-matched probe data points.

According to some embodiments, the processing circuitry may beconfigured to: identify a peak in the probe density histogram; assignprobe data points at a position across the width of the road segmentcorresponding to the peak in the probe density histogram a peak value;assign probe data points at positions along the width of the roadsegment values corresponding to their position relative to the peak inthe probe density histogram, where values assigned are inverselyproportional to a distance of the position across the width of the roadsegment from the peak; and causing the processing circuitry to identify,based on the probe density histogram, probe data points that aredetermined to be map-matched and classify the probe data pointsdetermined to be map-matched as map-matched probe data points byidentifying probe data points having a value satisfying a predeterminedthreshold as map-matched probe data points.

The processing circuitry may be configured to calculate, for probe datapoints from each probe apparatus, a probability that the respectiveprobe data points are map-matched based on a proportion of the probedata points from the respective probe apparatus corresponding to a peakin the probe density histogram, wherein a peak in the probe densityhistogram includes a relatively high proportion of probe data pointsgrouped to at least one of the plurality of positions across the widthof the road segment, where the relatively high proportion of probe datapoints may be established based on a statistically significant deviationat the at least one of the plurality of positions across the width ofthe road segment relative to the probe density histogram adjacent the atleast one of the plurality of positions. The processing circuitry may beconfigured to determine a confidence value of the probability for theprobe data points from each probe apparatus, where the confidence valueis determined based on a number of probe data points from a respectiveprobe apparatus, where the confidence value of the probability isproportional to a number of probe data points from the respective probeapparatus. The probe data points from a respective probe apparatus maybe identified as map-matched in response to the probability that therespective probe data points are map-matched satisfying a predeterminedprobability and the confidence value of the probability for the probedata points from the respective probe apparatus satisfying apredetermined confidence value.

Embodiments provided herein may include an apparatus having at least oneprocessor and at least one memory including computer program code. Thecomputer program code may be configured to, with the processor, causethe apparatus to at least: receive probe data points associated with aplurality of vehicles, each probe data point received from a probeapparatus of a plurality of probe apparatuses, each probe apparatus oneor more sensors and being onboard, attached, or otherwise associatedwith a respective vehicle, where each probe data point includes locationinformation associated with the respective probe apparatus; for each ofthe probe data points, determine a location and a road segmentcorresponding to the location; generate, from the probe data pointsassociated with a first road segment, a probe density histogram for thefirst road segment, where the probe density histogram represents avolume of probe data points at each of a plurality of positions across awidth of the road segment; identify, based on the probe densityhistogram, probe data points that are determined to be map-matched andclassify the probe data points determined to be map-matched asmap-matched probe data points; identify, based on the probe densityhistogram, probe data points that are determined to be non-map-matchedand classify the probe data points determined to be non-map-matched asnon-map-matched probe data points. The data points classified asmap-matched may be analyzed with respect to a position of a road segmentof a digital map corresponding to the map-matched road segment. Theprobe data points classified as non-map-matched may be analyzed toestablish one or more of lane position, lane-based traffic flow, or roadcenterline accuracy.

According to some embodiments, the probe data points associated with aplurality of vehicles may be received from a probe data provider, wherethe probe data provider may be classified according to whether the probedata provided is classified as map-matched, as non-map-matched, or as acombination of map-matched and non-map-matched. In response to the probedata provider being classified as providing a combination of map-matchedand non-map-matched probe data, a proportion of the probe data pointsclassified as map-matched and a proportion of the probe data pointsclassified as non-map-matched may be determined, and the probe dataprovider may be classified according to the proportions determined. Theprobe data points may be grouped into bins at each of the plurality ofpositions across the width of the road segment, where the apparatuscaused to identify probe data points that are determined to bemap-matched includes causing the apparatus to: identify a relativelyhigh proportion of probe data points grouped to at least one of theplurality of positions across the width of the road segment, where therelatively high proportion of probe data points may be established basedon a statistically significant deviation at the at least one of theplurality of positions across the width of the road segment relative tothe probe density histogram adjacent the at least one of the pluralityof positions; and identify the probe data points grouped to the at leastone of the plurality of positions across the width of the road segmentas map-matched and classify the probe data points grouped to the atleast one of the plurality of positions as map-matched probe datapoints.

The apparatus of some embodiments may be caused to: identify a peak inthe probe density histogram; assign probe data points at a positionacross the width of the road segment corresponding to the peak in theprobe density histogram a peak value; assign probe data points atpositions along the width of the road segment values corresponding totheir position relative to the peak in the probe density histogram,where values assigned are inversely proportional to a distance of theposition across the width of the road segment from the peak; and wherecausing the processing circuitry to identify, based on the probe densityhistogram, probe data points that are determined to be map-matched andclassify the probe data points determined to be map-matched asmap-matched probe data points includes identifying probe data pointshaving a value satisfying a predetermined threshold as map-matched probedata points.

The apparatus of example embodiments may be configured to calculate, forprobe data points from each probe apparatus, a probability that therespective probe data points are map-matched based on a proportion ofthe probe data points from the respective probe apparatus correspondingto a peak in the probe density histogram, wherein a peak in the probedensity histogram includes a relatively high proportion of probe datapoints grouped to at least one of the plurality of positions across thewidth of the road segment, where the relatively high proportion of probedata points may be established based on a statistically significantdeviation at the at least one of the plurality of positions across thewidth of the road segment relative to the probe density histogramadjacent the at least one of the plurality of positions. The apparatusmay be configured to determine a confidence value of the probability forthe probe data points from each probe apparatus, where the confidencevalue is determined based on a number of probe data points from arespective probe apparatus, where the confidence value of theprobability is proportional to a number of probe data points from therespective probe apparatus. The probe data points from a respectiveprobe apparatus may be identified as map-matched in response to theprobability that the respective probe data points are map-matchedsatisfying a predetermined probability and the confidence value of theprobability for the probe data points from the respective probeapparatus satisfying a predetermined confidence value.

Embodiments described herein may provide a computer program productincluding at least one non-transitory computer-readable storage mediumhaving computer-executable program code portions stored therein. Thecomputer-executable program code portions include program codeinstructions configured to: receive probe data points associated with aplurality of vehicles, each probe data point received from a probeapparatus of a plurality of probe apparatuses, each probe apparatusincluding one or more sensors and being onboard, associated with, orotherwise attached to a respective vehicle, where each probe data pointincludes location information associated with the respective probeapparatus; for each of the probe data points, determine a location and aroad segment corresponding to the location; generate, from the probedata points associated with a first road segment, a probe densityhistogram for the first road segment, where the probe density histogramrepresents a volume of probe data points at each of a plurality ofpositions across a width of the road segment; identify, based on theprobe density histogram, probe data points that are determined to bemap-matched and classify the probe data points determined to bemap-matched as map-matched probe data points; and identify, based on theprobe density histogram, probe data points that are determined to benon-map-matched and classify the probe data points determined to benon-map-matched as non-map-matched probe data points. The probe datapoints classified as map-matched may be analyzed with respect to aposition of a road segment of a digital map corresponding to themap-matched road segment. The probe data points classified asnon-map-matched may be analyzed to establish one or more of laneposition, lane-based traffic flow, or road centerline accuracy.

The probe data points associated with a plurality of vehicles may bereceived from a probe data provider, where the probe data provider maybe classified according to whether the probe data provider is classifiedas map-matched, as non-map-matched, or as a combination of map-matchedand non-map-matched. In response to the probe data provider beingclassified as providing a combination of map-matched and non-map-matchedprobe data, determining a proportion of the probe data points classifiedas map-matched and a proportion of the probe data points classified asnon-map-matched, and classifying the probe data provider according tothe proportions determined. The probe data points may be grouped intobins at each of the plurality of positions across the width of the roadsegment, where the processing circuitry configured to identify probedata points that are determined to be map-matched may include programcode instructions configured to: identify a relatively high proportionof probe data points grouped to at least one of the plurality ofpositions across the width of the road segment, where the relativelyhigh proportion of probe data points may be established based on astatistically significant deviation at the at least one of the pluralityof positions across the width of the road segment relative to the probedensity histogram adjacent the at least one of the plurality ofpositions; and identify the probe data points grouped to the at leastone of the plurality of positions across the width of the road segmentas map-matched and classify the probe data points grouped to the atleast one of the plurality of positions as map-matched probe datapoints.

Embodiments of the computer program product described herein may includeprogram code instructions configured to: identify a peak in the probedensity histogram; assign probe data points at a position across thewidth of the road segment corresponding to the peak in the probe densityhistogram a peak value; assign probe data points at positions along thewidth of the road segment values corresponding to their positionrelative to the peak in the probe density histogram, where valuesassigned are inversely proportional to a distance of the position acrossthe width of the road segment from the peak; and where causing theprocessing circuitry to identify, based on the probe density histogram,probe data points that are determined to be map-matched and classify theprobe data points determined to be map-matched probe data pointscomprises identifying probe data points having a value satisfying apredetermined threshold as map-matched probe data points.

The computer program product of example embodiments may include programcode instructions to calculate, for probe data points from each probeapparatus, a probability that the respective probe data points aremap-matched based on a proportion of the probe data points from therespective probe apparatus corresponding to a peak in the probe densityhistogram, wherein a peak in the probe density histogram includes arelatively high proportion of probe data points grouped to at least oneof the plurality of positions across the width of the road segment,where the relatively high proportion of probe data points may beestablished based on a statistically significant deviation at the atleast one of the plurality of positions across the width of the roadsegment relative to the probe density histogram adjacent the at leastone of the plurality of positions. The computer program product mayinclude program code instructions configured to determine a confidencevalue of the probability for the probe data points from each probeapparatus, where the confidence value is determined based on a number ofprobe data points from a respective probe apparatus, where theconfidence value of the probability is proportional to a number of probedata points from the respective probe apparatus. The probe data pointsfrom a respective probe apparatus may be identified as map-matched inresponse to the probability that the respective probe data points aremap-matched satisfying a predetermined probability and the confidencevalue of the probability for the probe data points from the respectiveprobe apparatus satisfying a predetermined confidence value.

Embodiments provided herein may include a method for establishingwhether probe data points are map-matched or not. Methods may include:receiving probe data points associated with a plurality of vehicles,each probe data point received from a probe apparatus of a plurality ofprobe apparatuses, each probe apparatus one or more sensors and beingonboard, attached, or otherwise associated with a respective vehicle,where each probe data point includes location information associatedwith the respective probe apparatus; for each of the probe data points,determining a location and a road segment corresponding to the location;generating, from the probe data points associated with a first roadsegment, a probe density histogram for the first road segment, where theprobe density histogram represents a volume of probe data points at eachof a plurality of positions across a width of the road segment;identifying, based on the probe density histogram, probe data pointsthat are determined to be map-matched and classify the probe data pointsdetermined to be map-matched as map-matched probe data points;identifying, based on the probe density histogram, probe data pointsthat are determined to be non-map-matched and classify the probe datapoints determined to be non-map-matched as non-map-matched probe datapoints. The data points classified as map-matched may be analyzed withrespect to a position of a road segment of a digital map correspondingto the map-matched road segment. The probe data points classified asnon-map-matched may be analyzed to establish one or more of laneposition, lane-based traffic flow, or road centerline accuracy.

According to some embodiments, the probe data points associated with aplurality of vehicles may be received from a probe data provider, wherethe probe data provider may be classified according to whether the probedata provided is classified as map-matched, as non-map-matched, or as acombination of map-matched and non-map-matched. In response to the probedata provider being classified as providing a combination of map-matchedand non-map-matched probe data, a proportion of the probe data pointsclassified as map-matched and a proportion of the probe data pointsclassified as non-map-matched may be determined, and the probe dataprovider may be classified according to the proportions determined. Theprobe data points may be grouped into bins at each of the plurality ofpositions across the width of the road segment, where the method ofidentifying probe data points that are determined to be map-matchedincludes: identifying a relatively high proportion of probe data pointsgrouped to at least one of the plurality of positions across the widthof the road segment, where the relatively high proportion of probe datapoints may be established based on a statistically significant deviationat the at least one of the plurality of positions across the width ofthe road segment relative to the probe density histogram adjacent the atleast one of the plurality of positions; and identifying the probe datapoints grouped to the at least one of the plurality of positions acrossthe width of the road segment as map-matched and classifying the probedata points grouped to the at least one of the plurality of positions asmap-matched probe data points.

The method of some embodiments may include: identifying a peak in theprobe density histogram; assigning probe data points at a positionacross the width of the road segment corresponding to the peak in theprobe density histogram a peak value; assigning probe data points atpositions along the width of the road segment values corresponding totheir position relative to the peak in the probe density histogram,where values assigned are inversely proportional to a distance of theposition across the width of the road segment from the peak; and whereidentifying, based on the probe density histogram, probe data pointsthat are determined to be map-matched and classifying the probe datapoints determined to be map-matched as map-matched probe data pointsincludes identifying probe data points having a value satisfying apredetermined threshold as map-matched probe data points.

The method of example embodiments may include calculating, for probedata points from each probe apparatus, a probability that the respectiveprobe data points are map-matched based on a proportion of the probedata points from the respective probe apparatus corresponding to a peakin the probe density histogram, wherein a peak in the probe densityhistogram includes a relatively high proportion of probe data pointsgrouped to at least one of the plurality of positions across the widthof the road segment, where the relatively high proportion of probe datapoints may be established based on a statistically significant deviationat the at least one of the plurality of positions across the width ofthe road segment relative to the probe density histogram adjacent the atleast one of the plurality of positions. Methods may include determininga confidence value of the probability for the probe data points fromeach probe apparatus, where the confidence value is determined based ona number of probe data points from a respective probe apparatus, wherethe confidence value of the probability is proportional to a number ofprobe data points from the respective probe apparatus. The probe datapoints from a respective probe apparatus may be identified asmap-matched in response to the probability that the respective probedata points are map-matched satisfying a predetermined probability andthe confidence value of the probability for the probe data points fromthe respective probe apparatus satisfying a predetermined confidencevalue.

Embodiments provided herein may include an apparatus having means forestablishing whether probe data points are map-matched or not. Theapparatus may include: means for receiving probe data points associatedwith a plurality of vehicles, each probe data point received from aprobe apparatus of a plurality of probe apparatuses, each probeapparatus one or more sensors and being onboard, attached, or otherwiseassociated with a respective vehicle, where each probe data pointincludes location information associated with the respective probeapparatus; for each of the probe data points, means for determining alocation and a road segment corresponding to the location; means forgenerating, from the probe data points associated with a first roadsegment, a probe density histogram for the first road segment, where theprobe density histogram represents a volume of probe data points at eachof a plurality of positions across a width of the road segment; meansfor identifying, based on the probe density histogram, probe data pointsthat are determined to be map-matched and classify the probe data pointsdetermined to be map-matched as map-matched probe data points; means foridentifying, based on the probe density histogram, probe data pointsthat are determined to be non-map-matched and classify the probe datapoints determined to be non-map-matched as non-map-matched probe datapoints. The data points classified as map-matched may be analyzed withrespect to a position of a road segment of a digital map correspondingto the map-matched road segment. The probe data points classified asnon-map-matched may be analyzed to establish one or more of laneposition, lane-based traffic flow, or road centerline accuracy.

According to some embodiments, the probe data points associated with aplurality of vehicles may be received from a probe data provider, wherethe probe data provider may be classified according to whether the probedata provided is classified as map-matched, as non-map-matched, or as acombination of map-matched and non-map-matched. In response to the probedata provider being classified as providing a combination of map-matchedand non-map-matched probe data, a proportion of the probe data pointsclassified as map-matched and a proportion of the probe data pointsclassified as non-map-matched may be determined, and the probe dataprovider may be classified according to the proportions determined. Theprobe data points may be grouped into bins at each of the plurality ofpositions across the width of the road segment, where the means foridentifying probe data points that are determined to be map-matched mayinclude: means for identifying a relatively high proportion of probedata points grouped to at least one of the plurality of positions acrossthe width of the road segment, where the relatively high proportion ofprobe data points may be established based on a statisticallysignificant deviation at the at least one of the plurality of positionsacross the width of the road segment relative to the probe densityhistogram adjacent the at least one of the plurality of positions; andmeans for identifying the probe data points grouped to the at least oneof the plurality of positions across the width of the road segment asmap-matched and classifying the probe data points grouped to the atleast one of the plurality of positions as map-matched probe datapoints.

The apparatus of some embodiments may include: means for identifying apeak in the probe density histogram; means for assigning probe datapoints at a position across the width of the road segment correspondingto the peak in the probe density histogram a peak value; means forassigning probe data points at positions along the width of the roadsegment values corresponding to their position relative to the peak inthe probe density histogram, where values assigned are inverselyproportional to a distance of the position across the width of the roadsegment from the peak; and where the means for identifying, based on theprobe density histogram, probe data points that are determined to bemap-matched and classifying the probe data points determined to bemap-matched as map-matched probe data points includes means foridentifying probe data points having a value satisfying a predeterminedthreshold as map-matched probe data points.

The apparatus of example embodiments may include means for calculating,for probe data points from each probe apparatus, a probability that therespective probe data points are map-matched based on a proportion ofthe probe data points from the respective probe apparatus correspondingto a peak in the probe density histogram, wherein a peak in the probedensity histogram includes a relatively high proportion of probe datapoints grouped to at least one of the plurality of positions across thewidth of the road segment, where the relatively high proportion of probedata points may be established based on a statistically significantdeviation at the at least one of the plurality of positions across thewidth of the road segment relative to the probe density histogramadjacent the at least one of the plurality of positions. Apparatuses mayinclude means for determining a confidence value of the probability forthe probe data points from each probe apparatus, where the confidencevalue is determined based on a number of probe data points from arespective probe apparatus, where the confidence value of theprobability is proportional to a number of probe data points from therespective probe apparatus. The probe data points from a respectiveprobe apparatus may be identified as map-matched in response to theprobability that the respective probe data points are map-matchedsatisfying a predetermined probability and the confidence value of theprobability for the probe data points from the respective probeapparatus satisfying a predetermined confidence value.

BRIEF DESCRIPTION OF THE DRAWINGS

Having thus described example embodiments of the invention in generalterms, reference will now be made to the accompanying drawings, whichare not necessarily drawn to scale, and wherein:

FIG. 1 illustrates a communications diagram in accordance with anexample embodiment of the present invention;

FIG. 2 is a block diagram of an apparatus that may be specificallyconfigured for deciphering map-matched probe data points fromnon-map-matched probe data points in accordance with an exampleembodiment of the present invention;

FIG. 3 is a map representation of a plurality of map-matched probe datapoints for a plurality of probes gathered in the region represented bythe map according to an example embodiment of the present invention;

FIG. 4 is a map representation of a plurality of both map-matched andnon-map-matched probe data points for a plurality of probes gathered inthe region represented by the map according to an example embodiment ofthe present invention;

FIG. 5 is a map depiction of a road segment including a plurality ofboth map-matched and non-map-matched probe data points and a probe datahistogram associated with the road segment;

FIG. 6 is a map representation of a plurality of both map-matched andnon-map-matched probe data points for a plurality of probes gathered inthe region represented by the map and indicating a confidence oflikelihood of the probe data points being map-matched according to anexample embodiment of the present invention; and

FIG. 7 is a flowchart of a method for determining whether probe datapoints are map-matched or non-map-matched according to an exampleembodiment of the present invention.

DETAILED DESCRIPTION

Some embodiments of the present invention will now be described morefully hereinafter with reference to the accompanying drawings, in whichsome, but not all, embodiments of the invention are shown. Indeed,various embodiments of the invention may be embodied in many differentforms and should not be construed as limited to the embodiments setforth herein; rather, these embodiments are provided so that thisdisclosure will satisfy applicable legal requirements. Like referencenumerals refer to like elements throughout. As used herein, the terms“data,” “content,” “information,” and similar terms may be usedinterchangeably to refer to data capable of being transmitted, receivedand/or stored in accordance with embodiments of the present invention.Thus, use of any such terms should not be taken to limit the spirit andscope of embodiments of the present invention.

As defined herein, a “computer-readable storage medium,” which refers toa non-transitory physical storage medium (for example, volatile ornon-volatile memory device), can be differentiated from a“computer-readable transmission medium,” which refers to anelectromagnetic signal.

A method, apparatus, and computer program product are provided herein inaccordance with an example embodiment for determining whether probe datapoints received from a provider are map-matched to a road geometry orraw data, not map-matched to the road geometry. Further, according tosome embodiments, a portion of probe data points received from aprovider may be map-matched, such that embodiments described hereindetermine the portion of map-matched probe data points and the portionthat are not map-matched. Distinguishing map-matched probe data pointsfrom non-map-matched probe data points is critical for establishing howthe probe data points may be used in map creation, map revision, trafficestimation, lane-based traffic or geometry data, or the like. Bothmap-matched and non-map-matched probe data points may be useful indistinct manners; however, it is imperative to be able to separate themap-matched probe data points from the non-map-matched probe datapoints. An indication of whether probe data points have been map-matchedor not is not typically provided by the provider of the probe datapoints such that it is desirable to establish a mechanism thatdetermines whether the probe data points have been map-matched in aconsistent, reliable, and efficient manner.

To determine probe data points that are map-matched and those that arenot map-matched, a system as illustrated in FIG. 1 may evaluate probedata points and distinguish map-matched probe data points fromnon-map-matched probe data points. FIG. 1 illustrates a communicationdiagram of an example embodiment of a system for implementing exampleembodiments described herein. The illustrated embodiment of FIG. 1includes a map developer system 116 including a processing server 102and a geographic map database 108 in data communication with a userequipment (UE) 104 through a network 112, and one or more mobile devices114. The mobile device 114 may be associated, coupled, or otherwiseintegrated with a vehicle, such as in a vehicle's head unit,infotainment unit, or an advanced driver assistance system (ADAS), forexample. Additional, different, or fewer components may be provided. Forexample, many mobile devices 114 may connect with the network 112. Themap developer 116 may include computer systems and a network of a systemoperator. The processing server 102 may include or otherwise be incommunication with the map database 108, which may be embodied as aremote map server. The network may be wired, wireless, or anycombination of wired and wireless communication networks, such ascellular, Wi-Fi, internet, local area networks, or the like.

The user equipment 104 may include a mobile computing device such as alaptop computer, tablet computer, mobile phone, smart phone, navigationunit, personal data assistant, watch, camera, or the like. Processingserver 102 may be one or more fixed or mobile computing devices. Theuser equipment 104 may be configured to access the map database 108 viathe processing server 102 through, for example, a mapping application,such that the user equipment may provide navigational assistance to auser among other services provided through access to the map developer116.

The map database 108 may include node data, road segment data or linkdata, point of interest (POI) data, or the like. The map database 108may also include cartographic data, routing data, and/or maneuveringdata. According to some example embodiments, the road segment datarecords may be links or segments representing roads, streets, or paths,as may be used in calculating a route or recorded route information fordetermination of one or more personalized routes. The node data may beend points corresponding to the respective links or segments of roadsegment data. The road link data and the node data may represent a roadnetwork, such as used by vehicles, cars, trucks, buses, motorcycles,and/or other entities. Optionally, the map database 108 may contain pathsegment and node data records or other data that may representpedestrian paths or areas in addition to or instead of the vehicle roadrecord data, for example. The road/link segments and nodes can beassociated with attributes, such as geographic coordinates, streetnames, address ranges, speed limits, turn restrictions at intersections,and other navigation related attributes, as well as POIs, such asfueling stations, hotels, restaurants, museums, stadiums, offices, autorepair shops, buildings, stores, parks, etc. The map database 108 caninclude data about the POIs and their respective locations in the POIrecords. The map database 108 may include data about places, such ascities, towns, or other communities, and other geographic features suchas bodies of water, mountain ranges, etc. Such place or feature data canbe part of the POI data or can be associated with POIs or POI datarecords (such as a data point used for displaying or representing aposition of a city). In addition, the map database 108 can include eventdata (e.g., traffic incidents, construction activities, scheduledevents, unscheduled events, etc.) associated with the POI data recordsor other records of the map database 108.

The map database 108 may be maintained by a content provider e.g., a mapdeveloper. By way of example, the map developer can collect geographicdata to generate and enhance the map database 108. There can bedifferent ways used by the map developer to collect data. These ways caninclude obtaining data from other, third party sources, such asmunicipalities or respective geographic authorities, mobile phoneservice providers, vehicle manufacturers, navigation systemdevelopers/providers, or the like. In addition, the map developer canemploy field personnel to travel by vehicle along roads throughout thegeographic region to observe features and/or record information aboutthem, for example. Also, remote sensing, such as aerial or satellitephotography, can be used to generate map geometries directly or throughmachine learning.

The map database 108 may be a master map database stored in a formatthat facilitates updating, maintenance, and development. For example,the master map database or data in the master map database can be in anOracle spatial format or other spatial format, such as for developmentor production purposes. The Oracle spatial format ordevelopment/production database can be compiled into a delivery format,such as a geographic data files (GDF) format. The data in the productionand/or delivery formats can be compiled or further compiled to formgeographic database products or databases, which can be used in end usernavigation devices or systems.

For example, geographic data may be compiled (such as into a platformspecification format (PSF) format) to organize and/or configure the datafor performing navigation-related functions and/or services, such asroute calculation, route guidance, map display, speed calculation,distance and travel time functions, and other functions, by a navigationdevice, such as by user equipment 104, for example. The compilation toproduce the end user databases can be performed by a party or entityseparate from the map developer. For example, a customer of the mapdeveloper, such as a navigation device developer or other end userdevice developer, can perform compilation on a received map database ina delivery format to produce one or more compiled navigation databases.

As mentioned above, the server side map database 108 may be a mastergeographic database, but in alternate embodiments, a client side mapdatabase 108 may represent a compiled navigation database that may beused in or with end user devices (e.g., user equipment 104) to providenavigation and/or map-related functions. For example, the map database108 may be used with the end user device 104 to provide an end user withnavigation features. In such a case, the map database 108 can bedownloaded or stored on the end user device (user equipment 104) whichcan access the map database 108 through a wireless or wired connection,such as via a processing server 102 and/or the network 112, for example.

In one embodiment, the end user device or user equipment 104 can be anavigation system, such as an in-vehicle navigation system, such as anADAS, a personal navigation device (PND), or a portable navigationdevice, a cellular telephone, a smart phone, a personal digitalassistant (PDA), a watch, a camera, a computer, and/or another devicethat can perform navigation-related functions, such as digital routingand map display. An end user can use the user equipment 104 fornavigation and map functions such as guidance and map display, forexample, and for determination of one or more personalized routes orroute segments based on one or more calculated and recorded routes,according to some example embodiments.

The processing server 102 may receive probe data, directly orindirectly, from a mobile device 114. The mobile device 114 may includeone or more detectors or sensors as a positioning system built orembedded into or within the interior of the mobile device 114.Alternatively, the mobile device 114 uses communications signals forposition determination. The mobile device 114 may receive location datafrom a positioning system, such as a global positioning system (GPS),cellular tower location methods, access point communicationfingerprinting, or the like. The server 102 may receive sensor dataconfigured to describe a position of a mobile device, or a controller ofthe mobile device 114 may receive the sensor data from the positioningsystem of the mobile device 114. The mobile device 114 may also includea system for tracking mobile device movement, such as rotation,velocity, or acceleration. Movement information may also be determinedusing the positioning system. The mobile device 114 may use thedetectors and sensors to provide data indicating a location of avehicle. This vehicle data, also referred to herein as “probe data”, maybe collected by any device capable of determining the necessaryinformation, and providing the necessary information to a remote entity.The mobile device 114 is one example of a device that can function as aprobe to collect probe data of a vehicle.

More specifically, probe data (e.g., collected by mobile device 114) maybe representative of the location of a vehicle at a respective point intime and may be collected while a vehicle is traveling along a route.While probe data is described herein as being vehicle probe data,example embodiments may be implemented with pedestrian probe data,marine vehicle probe data, or non-motorized vehicle probe data (e.g.,from bicycles, skate boards, horseback, etc.). According to the exampleembodiment described below with the probe data being from motorizedvehicles traveling along roadways, the probe data may include, withoutlimitation, location data, (e.g. a latitudinal and longitudinalposition, and/or height, GPS coordinates, proximity readings associatedwith a radio frequency identification (RFID) tag, or the like), rate oftravel, (e.g. speed), direction of travel, (e.g. heading, cardinaldirection, or the like), device identifier, (e.g. vehicle identifier,user identifier, or the like), a time stamp associated with the datacollection, or the like. The mobile device 114, may be any devicecapable of collecting the aforementioned probe data. Some examples ofthe mobile device 114 may include specialized vehicle mapping equipment,navigational systems, mobile devices, such as phones or personal dataassistants, or the like.

While probe data points may be collected as raw data (e.g., includingraw location data, heading, speed, time, or any other relevant/availabledata), probe data may also be collected from third party sources, suchas third party 120. As noted above, a third party may include amunicipality, a governmental entity, a mobile phone service provider, anavigation system provider, a vehicle manufacturer, or the like. Devicesthat can generate probe data points are ubiquitous and can take avariety of forms such that numerous entities may have access to datathat is considered probe data, and any of these entities may contributeto the map developer system 116 through sharing of probe data pointinformation. While some third parties 120 may have access to copiousamounts of personal data or individually-identifying data (e.g., amobile phone number, an account holder, a vehicle owner, etc.), probedata points provided by the third party 120 to the network 112 may belimited to anonymized data or probe data points that contain onlyinformation that is beneficial to crowd-sourcing applications withoutthe need for personally identifying information. The third party 120 may“scrub” or anonymize the probe data points before sending to the mapdeveloper 116 via the network 112, or the map developer 116 mayanonymize the data upon receipt if it contains data extraneous to thecore functionality of the map developer.

Given the breadth of entities that may qualify as third party providers120 according to example embodiments described herein, the third partyprovider systems may vary widely in terms of the components of thesystem and the level of information and information detail contained inprobe data points gathered by the third party 120. A third partyprovider that is a sophisticated location-enabled service, such as amobile phone service provider, may include a map database 128 andprocessing server 122 of their own through which they can locate users,such as through GPS or through cellular signal triangulation. Such athird party provider 120 may include a map database 128 having digitalmap geometry stored therein onto which probe data points may beprojected. The processing server may include some or all of thefunctionality of the processing server 102 of the map developer 116described above. The map database 128 of the third party provider 120may be configured in a similar manner as the map database 108 describedabove. The map database 128 of the third party provider 120 may be,itself, provided by another entity to the third party provider. Thethird party provider 120 may also have a probe data point database 126which collects and stores probe data points of users of the third partyprovider 120. The probe data point database may gather probe data pointsin real-time or periodically from users, depending upon the planned useof such data points. The probe data point database 126, in cooperationwith the processing server 122, may provide the probe data points to themap developer 116 via the network 112.

According to some embodiments, the third party provider 120 may processsome or all of the probe data points before storage in the probe datapoint database 126 and/or before providing to the map developer 116. Theprocessing of probe data points may include normalization of data, suchas map-matching probe data points with locations on a map, such as inmap database 128. For example, in an embodiment in which the third partyprovider is a vehicle manufacturer or a navigation system manufacturer,the probe data points collected by the third party 120, which may befrom user equipment 104, for example, may be map-matched to roadwaysincluding road segments or road links within the map database as probedata points from a vehicle are typically going to be from travel along aroadway. In this manner, the third party provider 120 may compensate forGPS errors or other measurement errors that may place probe data pointsoutside of the bounds of a typical roadway. However, not all third partyproviders 120 may normalize or map-match probe data points to a map, andeven those third party providers that do map-match probe data points,they may not map-match all of the probe data points they collect. Assuch, the probe data points provided by third party providers 120 to themap developer 116 may or may not be map-matched, and the probe datapoints may not contain an indication as to whether they are map-matchedor not.

For certain types of processing of probe data points it may beimperative to know whether the probe data points are map-matched or not.For example, probe data points that are not map-matched may be raw probedata points and processing of these probe data points may be done by themap developer 116. The map developer may be able to use suchnon-map-matched probe data points to establish lane-level locationswithin a roadway or the accuracy of the map database 108 based on thegathered non-map-matched probe data points by evaluating how closely theprobe data points align with road segments or links within the mapdatabase. The non-map-matched probe data points may be considered “raw”data points whereby the map developer has the ability to process thisdata as desired without concern as to whether the data is trulyrepresentative of the reported location, time, heading, etc. of theprobe data point from which it came.

While non-map-matched probe data points can be useful for a mapdeveloper system as described above, map-matched probe data points mayalso be beneficial. For example, if the map developer 116 receives probedata points that are determined to be map-matched, the map-matched probedata points can be used to compare against the map of the map database108. Map-matched probe data points are typically map-matched to aroadway center. If a roadway (e.g., road segment or link) is defined bymap-matched probe data, that defined roadway may be compared against thecorresponding roadway in the map database 108. This comparison canidentify any discrepancies between the map developer map database 108and the map database 128 of the third party provider 120 that providedthe map-matched probe data points. Based on this discrepancy, an errorcan be identified in the map database of the map developer 116 or thethird party provider 120 and rectified accordingly.

An example embodiment of a processing server 102 of map developer 116may be embodied in an apparatus as illustrated in FIG. 2. The apparatus,such as that shown in FIG. 2, may be specifically configured inaccordance with an example embodiment of the present invention fordetermining whether probe data points are map-matched or not.Map-matched probe data points are described herein as probe data pointsthat have been normalized from their original, raw location data tocorrespond to a road segment proximate the original, raw location data.Conversely, non-map-matched probe data points are probe data points thatretain their raw location data that has not been normalized tocorrespond to a road segment. The apparatus 200 may include or otherwisebe in communication with a processor 202, a memory device 204, acommunication interface 206, and a user interface 208. In someembodiments, the processor (and/or co-processors or any other processingcircuitry assisting or otherwise associated with the processor) may bein communication with the memory device via a bus for passinginformation among components of the apparatus. The memory device may benon-transitory and may include, for example, one or more volatile and/ornon-volatile memories. In other words, for example, the memory devicemay be an electronic storage device (for example, a computer readablestorage medium) comprising gates configured to store data (for example,bits) that may be retrievable by a machine (for example, a computingdevice like the processor 202). The memory device may be configured tostore information, data, content, applications, instructions, or thelike, for enabling the apparatus to carry out various functions inaccordance with an example embodiment of the present invention. Forexample, the memory device could be configured to buffer input data forprocessing by the processor. Additionally or alternatively, the memorydevice could be configured to store instructions for execution by theprocessor.

The processor 202 may be embodied in a number of different ways. Forexample, the processor may be embodied as one or more of varioushardware processing means such as a coprocessor, a microprocessor, acontroller, a digital signal processor (DSP), a processing element withor without an accompanying DSP, or various other processing circuitryincluding integrated circuits such as, for example, an ASIC (applicationspecific integrated circuit), an FPGA (field programmable gate array), amicrocontroller unit (MCU), a hardware accelerator, a special-purposecomputer chip, or the like. As such, in some embodiments, the processormay include one or more processing cores configured to performindependently. A multi-core processor may enable multiprocessing withina single physical package. Additionally or alternatively, the processormay include one or more processors configured in tandem via the bus toenable independent execution of instructions, pipelining and/ormultithreading.

In an example embodiment, the processor 202 may be configured to executeinstructions stored in the memory device 204 or otherwise accessible tothe processor. Alternatively or additionally, the processor may beconfigured to execute hard coded functionality. As such, whetherconfigured by hardware or software methods, or by a combination thereof,the processor may represent an entity (for example, physically embodiedin circuitry) capable of performing operations according to anembodiment of the present invention while configured accordingly. Thus,for example, when the processor is embodied as an ASIC, FPGA or thelike, the processor may be specifically configured hardware forconducting the operations described herein. Alternatively, as anotherexample, when the processor is embodied as an executor of softwareinstructions, the instructions may specifically configure the processorto perform the algorithms and/or operations described herein when theinstructions are executed. However, in some cases, the processor may bea processor specific device (for example, a mobile terminal or a fixedcomputing device) configured to employ an embodiment of the presentinvention by further configuration of the processor by instructions forperforming the algorithms and/or operations described herein. Theprocessor may include, among other things, a clock, an arithmetic logicunit (ALU) and logic gates configured to support operation of theprocessor.

The apparatus 200 of an example embodiment may also include acommunication interface 206 that may be any means such as a device orcircuitry embodied in either hardware or a combination of hardware andsoftware that is configured to receive and/or transmit data to/from acommunications device in communication with the apparatus, such as tofacilitate communications with one or more user equipment 104 or thelike. In this regard, the communication interface may include, forexample, an antenna (or multiple antennae) and supporting hardwareand/or software for enabling communications with a wirelesscommunication network. Additionally or alternatively, the communicationinterface may include the circuitry for interacting with the antenna(s)to cause transmission of signals via the antenna(s) or to handle receiptof signals received via the antenna(s). In some environments, thecommunication interface may alternatively or also support wiredcommunication. As such, for example, the communication interface mayinclude a communication modem and/or other hardware and/or software forsupporting communication via cable, digital subscriber line (DSL),universal serial bus (USB) or other mechanisms.

The apparatus 200 may also include a user interface 208 that may in turnbe in communication with the processor 202 to provide output to the userand, in some embodiments, to receive an indication of a user input. Assuch, the user interface may include a display and, in some embodiments,may also include a keyboard, a mouse, a joystick, a touch screen, touchareas, soft keys, one or more microphones, a plurality of speakers, orother input/output mechanisms. In one embodiment, the processor maycomprise user interface circuitry configured to control at least somefunctions of one or more user interface elements such as a display and,in some embodiments, a plurality of speakers, a ringer, one or moremicrophones and/or the like. The processor and/or user interfacecircuitry comprising the processor may be configured to control one ormore functions of one or more user interface elements through computerprogram instructions (for example, software and/or firmware) stored on amemory accessible to the processor (for example, memory device 204,and/or the like).

Example embodiments of the present invention may provide a mechanism forestablishing whether probe data points are map-matched or not.Embodiments described herein relate to identifying probe trajectoriesthat have been map-matched by a provider of the probe data point. When aprobe data point has been map-matched, the original probe data pointlocation (e.g., latitude, longitude, and possibly altitude) have beenprojected (i.e., moved) from its original (noisy) GPS location on to theroad segment centerline of the map in the map database. Therefore, whena probe data point provider has map-matched its probe data points, theywill generally reside on the road segment centerline of the map geometrythat was used for map matching. FIG. 3 illustrates an example embodimentof probe data that has been map-matched. As shown, the probe data points310 align with the roadways of the map 300 and more particularly, alignwith one another such that there is little “noise” or probe data pointsoutside of roadway paths.

However, third party providers often aggregate probe data points frommultiple sources that may contain a mix of map-matched andnon-map-matched probe data point trajectories, as shown in FIG. 4. Asshown, the probe data points 410 are noisy around the roadways of themap 400. While generally concentrated around roadway centers, it isevident that at least some of the probe data points of FIG. 4 are notmap-matched as they lie significantly outside of the roadways of the map400. In cases such as that of FIG. 4, however, it is not readilyapparent which probe data points, if any, have been map-matched. It isimperative for certain data processing methods to be able to distinguishprobe data points that have been map-matched from those that have notbeen map-matched.

Map-matching of probe data points introduces bias and artifacts in theprobe data points and may negatively influence probe data processingtechniques, such as inferring road center geometry, stay pointidentification, and detecting lane geometry and lane attributes. Beingable to identify provider map-matched probe data points enables thesegregation of map-matched probe data points from non-map-matched probedata points which can then be processed using the aforementioned probedata processing techniques among other processing methods. Retainingonly map-matched probe data points enables the identification of thirdparty provider road geometry that may be useful for change detection inroadway paths to help keep the map database 108 of the map serviceprovider 116 up to date.

In order to identify which probe data points have been map-matched(e.g., projected onto their map centerline geometry) by the probe dataprovider, embodiments described herein compute a provider map-matchedconfidence value based on the fraction of vertices of the probe datapoints that have been map matched. To determine if a vertex of a probedata point has been map-matched or not, a probe density histogram isfirst created. There are two manners in which such histograms can becreated. The first method is to divide the area proximate a road segmentinto a grid and determine the number of probe data points in each cellof the grid. This is a two-dimensional histogram, where the value ofeach cell of the map proximate the road segment includes a valuerepresenting the number of probe data points in that cell. The secondmethod is a one-dimensional histogram of a cross-section of a roadsegment where the distance dimension along the road segment is removed.This road section cross section histogram for each link is created usinghistogram bins that discretize the map-matching distance to the road mapcenterline based on the assumption that the road centerline geometry isfairly similar and parallel to the third party provider map geometry.Two-dimensional histogram cells or one-dimensional road cross sectionbins that are map-matched form spikes, as shown in FIG. 5, with respectadjacent cells or bins perpendicular to the road direction.

Creating a two-dimensional histogram has an advantage in that nomap-matching against a mapped road segment of the map database 108 isnecessary, but a two-dimensional histogram produced according to themethods described herein may require higher probe density than theone-dimensional histogram. Further, low probe density or non-uniformprobe data point density may yield insufficient probe data point densityto detect all cells that correspond to the third party provider mapgeometry.

Creating a one-dimensional cross-section histogram of the road geometrymay provide the advantage that it can be matched to the road segment ofthe map database 108 of the map service provider to match distancesalong the entire road link, thus requiring lower probe data pointdensity to form and identify the histogram peaks in the data from thethird party provider. However, the one-dimensional cross-sectionhistogram is based on an assumption that the map road centerlinegeometry of the map database 108 is substantially parallel to the thirdpart provider map road centerline geometry. Any misalignment between thetwo maps may artificially widen the width of the third party providermap matched spike location and thus yield false positives forclassifying probe data point vertices as map-matched.

FIG. 5 illustrates a road segment 510 of a map 500 where at least someof the probe data points are not map-matched as evidenced by the noisearound the road segment 510. The histogram 520 of FIG. 5 illustrates theprobe data point volume along the y-axis and the position across theroadway cross section along the x-axis. As shown, a spike 530 exists inthe probe data of the histogram, but so too does an apparent normaldistribution 540 of probe data points, suggesting that some, but not allof the probe data points shown are map-matched. While FIG. 5 illustratesa substantially normal distribution, the histogram may take any formbased on the probe data points received. This could include multi-modaldistribution, skewed distribution, comb distribution, edge peakdistribution, etc.

Once the histogram has been generated, as shown in FIG. 5, using theprobe data points and either the one-dimensional histogram or thetwo-dimensional histogram, the next step is to determine which cells(2D) or bins (1D) correspond to the third party provider's map location.Probe data points that are not map-matched will spread out in adistribution along the x-axis of the histogram due to GPS noise, and thespread may extend beyond the road surface width. However, probe datapoints that are provider map-matched may be projected onto their roadmap centerline and thus accumulate to form spikes for both histogramtypes. Identification of such map-matched artifact spikes can beaccomplished using example embodiments herein through robust statisticstechniques, including the median. If the histogram cell/bin value issignificantly larger than the neighborhood median, then it constitutes aspike for the cell/bin. Optionally, discrete edge detection techniquessuch as the Sobel operator may be used to identify spike locations inthe histogram. Using a binary classification scheme, a value of one canbe assigned to cells/bins of the histogram on the spike location, and azero value to all other cells/bins.

While the example embodiment of FIG. 5 illustrates a single spike orpeak, embodiments may include a plurality of peaks. Multiple peaks canbe indicative of a probe data points map-matched to different lanesacross a multi-lane roadway, or potentially represent probe data pointsmap-matched to two different map geometries, such as with probe datapoints coming from multiple sources. In either scenario, thedetermination as to whether the probe data points are map-matched may beperformed in a similar manner. If probe data points are map-matched totwo different map geometries, the probe data points may be fromdifferent providers and may be separated by provider as needed for probedata analysis. This may be done before or after determining whether theprobe data points are map-matched as will be apparent based on themethods described herein for establishing whether probe data points aremap-matched or not.

According to an example embodiment including a two-dimensional array ofprobe data points covering a geographic area, for a two-dimensional areahistogram cell:

$c_{ij} = \left\{ \begin{matrix}1 & {{if}\mspace{14mu} {spike}\mspace{14mu} {cell}} \\0 & {{if}\mspace{14mu} {non}\mspace{14mu} {spike}\mspace{14mu} {cell}}\end{matrix} \right.$

According to an example embodiment including a one-dimensional array ofprobe data points across the width of a road segment, for aone-dimensional cross-section histogram:

$b_{k} = \left\{ \begin{matrix}1 & {{if}\mspace{14mu} {spike}\mspace{14mu} {bin}} \\0 & {{if}\mspace{14mu} {non}\mspace{14mu} {spike}\mspace{14mu} {bin}}\end{matrix} \right.$

The cell/bin classification can also be non-binary, with a range from[0,1] or modeled, such as using a bell curve or parabola modeling ofdecreasing confidence the further away from the center peak location.With such a non-binary classification scheme, example embodiments couldcompensate for any inaccuracy in the location of the map cells/bins. Onesuch classification scheme may be based on the classification valuebeing one at the peak center and decreasing to zero some distance,D_(max), away from the road center geometry. The value of D_(max) could,for example, be defined as:

$D_{\max} = \frac{{peak\_ width} + {cell\_ width}}{2}$

Using a parabolic drop-off function, such as

${y(d)} = {{MAX}\left( {0,{1 - \left( \frac{d}{D_{\max}} \right)^{2}}} \right)}$

would yield:For two-dimensional area histogram cells:

$c_{ij} = \left\{ \begin{matrix}{{MAX}\left( {0,{1 - \left( \frac{d}{D_{\max}} \right)^{2}}} \right)} & {{if}\mspace{14mu} {spike}\mspace{14mu} {cell}} \\0 & {{if}\mspace{14mu} {non}\mspace{14mu} {spike}\mspace{14mu} {cell}}\end{matrix} \right.$

And for one-dimensional cross-section histograms:

$b_{k} = \left\{ \begin{matrix}{{MAX}\left( {0,{1 - \left( \frac{d}{D_{\max}} \right)^{2}}} \right)} & {{if}\mspace{14mu} {spike}\mspace{14mu} {bin}} \\0 & {{if}\mspace{14mu} {non}\mspace{14mu} {spike}\mspace{14mu} {bin}}\end{matrix} \right.$

Another non-binary classification scheme could be based on the histogrampeak value

_(ij) divided by the maximum histogram peak value

_(max):

Using a drop-off function, such as for two-dimensional area histogramcells:

$c_{ij} = \left\{ \begin{matrix}{h_{ij}/h_{\max}} & {{if}\mspace{14mu} {spike}\mspace{14mu} {cell}} \\0 & {{if}\mspace{14mu} {non}\mspace{14mu} {spike}\mspace{14mu} {cell}}\end{matrix} \right.$

And for one-dimensional cross-section histograms:

$b_{k} = \left\{ \begin{matrix}{h_{k}/h_{\max}} & {{if}\mspace{14mu} {spike}\mspace{14mu} {bin}} \\0 & {{if}\mspace{14mu} {non}\mspace{14mu} {spike}\mspace{14mu} {bin}}\end{matrix} \right.$

In order to determine for each probe data point if it is map-matched ornot, each probe data point may be assigned the cell/bin classificationvalue c_(ij) or b_(k) of the cell/bin into which the probe data pointfalls. While this technique can be used to establish which probe datapoints correspond to a map-matched position in the histogram,embodiments described herein may determine if probe data is map matchedbased on the trajectory of a respective probe apparatus. A trajectoryfor a probe apparatus includes a plurality of probe data pointsgenerated by a single probe apparatus, resulting in a trajectory of thepath of that probe apparatus. Each trajectory can be analyzed toestablish whether the probe data points from the corresponding probeapparatus are map matched, even if some of the probe data points do notfall within a peak of a histogram. For example, if a trajectory includesten probe data points, and nine of them are established to bemap-matched, it is likely that the 10^(th) probe data point is alsomap-matched, even if it does not fall into a peak of the histogram. Toevaluate whether a trajectory from a probe apparatus is map-matched bythe provider, a probability may be computed as to whether it is likelythat the probe data points of that trajectory are map-matched.

According to an example embodiment, the unadjusted probability p that atrajectory with

probe data point vertices is provider map matched may be calculatedbased on the following:

${p\left( {{is}\mspace{14mu} {provider}\mspace{14mu} {map}\mspace{14mu} {matched}} \right)} = {\frac{1}{n}{\sum\limits_{i = 1}^{n}v_{i}}}$

Where ν_(i) is the provider map match classification value according towhich bin/cell a vertex (probe data point) within the trajectory falls,and

is the number of vertices or probe data points within the trajectory fora respective probe apparatus.

However, for trajectories with few probe data points (vertices) therewould be a low confidence in the probe data point probability value, andfor trajectories with a large number of probe data points there would bea high confidence in the probability value.

To determine a methodology for lowering the probability value as afunction of the count of probe data points, a Margin of Error (ME) frompoll survey statistics can be used to lower our confidence as a functionof the number of probe data point vertices in a trajectory. The marginof error may be computed for a desired Confidence Level, e.g., 95%. Themargin of error is a statistic that describes the amount of randomsampling error in a survey. The larger the margin of error, the lessconfidence in the estimate p (probability) for whether the probe datapoints for a trajectory are map-matched or not. The margin of error maybe computed as the

-score,

times the standard error (SE), where the standard error for a sampleproportion p is:

${SE} = \sqrt{\frac{p\left( {1 - p} \right)}{n}}$

So that the margin of error becomes:

${ME} = {z\sqrt{\frac{p\left( {1 - p} \right)}{n}}}$

The

-score, which is the number of standard deviations from the mean atwhich a point is located for a desired Confidence Level, e.g., 95%. Fora 95% Confidence Interval, i.e., P(−z≤Z≤z)=0.95, we have:

P(−z≤Z≤z)=1−α=0.95, yielding α=0.05

So that the cumulative distribution function

${{\Phi (z)} = {{1 - \frac{\alpha}{2}} = {{1 - \frac{0.05}{2}} = 0.975}}},$

yielding a

-score of:

z=1/Φ⁻¹(0.975)=1.96

Where Φ(z) denotes the standard normal cumulative distribution functionwhich describes the probability that a real-valued random variable zwith a given probability distribution, such as a normal probabilitydistribution, will be found to have a value less than or equal to z.Thus, Φ⁻¹(x) is the inverse standard normal cumulative distributionfunction. By using the margin of error, the probability p that atrajectory with

probe data point vertices is map-matched by a third party provider islowered by subtracting the margin of error:

$p_{adjusted} = {{{MAX}\left( {0,{p_{unadjusted} - {ME}}} \right)} = {{MAX}\left( {0,{p_{unadjusted} - {z\sqrt{\frac{p\left( {1 - p} \right)}{n}}}}} \right)}}$

However, the standard error (SE) may be close to zero for a p valueclose to zero and one. Thus, if a trajectory with few probe data pointvertices (e.g., two), all fall within spike cells/bins, p=1, so themargin of error becomes zero, thus not reducing the confidence.Therefore, a maximum margin of error that occurs for p=0.5 may be used.By doing so, the formula becomes:

$p_{adjusted} = {{MAX}\left( {0,{p_{unadjusted} - {z\sqrt{\frac{0.5\left( {1 - 0.5} \right)}{n}}}}} \right)}$

Which can be rewritten as:

$p_{adjusted} = {{MAX}\left( {0,{{\frac{1}{n}{\sum\limits_{i = 1}^{n}v_{i}}} - {z\sqrt{\frac{0.25}{n}}}}} \right)}$

Where ν_(i) is the provider map match classification value according towhich bin/cell the probe data point vertex falls within,

is the number of probe data point vertices in the trajectory, and

is the z-value for the desired confidence interval.

FIG. 6 illustrates a color-coded map of probe data points where probedata points/trajectories that are determined to be map-matched with ahigh degree of confidence or probability (i.e., close to one) fall alongthe centerlines of the roadways of the map 600, while the probe datapoints/trajectories with a low degree of confidence or probability(i.e., close to zero) are noisy and lie in a spread-out distributionalong the road ways, as one would expect non-map-matched probe datapoints to fall. The probability thresholds for whether a trajectory ismap-matched can be user-adjusted as desired such that if the minimumprobability threshold is lowered, a denser road geometry may beestablished similar to that of FIG. 3. However, some trajectories mayonly have a few probe data points. When the trajectory probe data pointcount is low, such as one or two probe data point vertices (or belowsome minimum threshold), the trajectory may be established asnon-classifiable since the confidence in either classification valuewould be low due to the low probe data point vertex count. Therefore,only trajectories with high vertex counts are used to circumvent thisissue and provide reliable estimation as to whether probe data pointsare map-matched.

FIG. 7 illustrates a flowchart illustrative of a method according toexample embodiments of the present invention. It will be understood thateach block of the flowcharts and combination of blocks in the flowchartsmay be implemented by various means, such as hardware, firmware, aprocessor, circuitry, and/or other communication devices associated withexecution of software including one or more computer programinstructions. For example, one or more of the procedures described abovemay be embodied by computer program instructions. In this regard, thecomputer program instructions which embody the procedures describedabove may be stored by a memory device 204 of an apparatus employing anembodiment of the present invention and executed by a processor 202 ofthe apparatus. As will be appreciated, any such computer programinstructions may be loaded onto a computer or other programmableapparatus (for example, hardware) to produce a machine, such that theresulting computer or other programmable apparatus implements thefunctions specified in the flowchart blocks. These computer programinstructions may also be stored in a computer-readable memory that maydirect a computer or other programmable apparatus to function in aparticular manner, such that the instructions stored in thecomputer-readable memory produce an article of manufacture the executionof which implements the function specified in the flowchart blocks. Thecomputer program instructions may also be loaded onto a computer orother programmable apparatus to cause a series of operations to beperformed on the computer or other programmable apparatus to produce acomputer-implemented process such that the instructions, which executeon the computer or other programmable apparatus, provide operations forimplementing the functions specified in the flowchart blocks.

Accordingly, blocks of the flowcharts support combinations of means forperforming the specified functions and combinations of operations forperforming the specified functions for performing the specifiedfunctions. It will also be understood that one or more blocks of theflowcharts, and combinations of blocks in the flowcharts, can beimplemented by special purpose hardware-based computer systems, whichperform the specified functions, or combinations of special purposehardware and computer instructions.

FIG. 7 illustrates a method of determining whether probe data points aremap-matched or non-map-matched such that the probe data can be processedand analyzed appropriately without introducing bias into the analysis,which may be caused by map-matching ahead of analysis. At 610, probedata points are received, where the probe data points are generated byprobe apparatuses associated with a plurality of vehicles. The probeapparatuses may include a plurality of sensors and may be on, within, orotherwise attached to a vehicle. Each probe data point may includelocation information associated with the respective probe apparatus. Foreach of the probe data points, a location and an associated road segmentcorresponding to the location may be determined as shown at 620. Fromthe probe data points associated with the first road segment, a probedensity histogram for the road segment may be generated as shown at 630,where the probe density histogram represents a volume of probe datapoints at each of a plurality of positions across a width of the roadsegment. Based on the probe density histogram, probe data points thatare determined to be map-matched are classified as such as shown at 640,while probe data points determined, based on the probe densityhistogram, to be non-map-matched are classified as such at 650. Theprobe data points classified as map-matched may be analyzed with respectto a position of a road segment of a digital map corresponding to themap-matched road segment. Probe data points classified asnon-map-matched may be analyzed to establish one or more of laneposition, lane-based traffic flow, or road center line accuracy.

In an example embodiment, an apparatus for performing the method of FIG.7 above may comprise a processor (e.g., the processor 202) configured toperform some or each of the operations (610-650) described above. Theprocessor may, for example, be configured to perform the operations(610-650) by performing hardware implemented logical functions,executing stored instructions, or executing algorithms for performingeach of the operations. Alternatively, the apparatus may comprise meansfor performing each of the operations described above. In this regard,according to an example embodiment, examples of means for performingoperations 610-650 may comprise, for example, the processor 202 and/or adevice or circuit for executing instructions or executing an algorithmfor processing information as described above.

Many modifications and other embodiments of the inventions set forthherein will come to mind to one skilled in the art to which theseinventions pertain having the benefit of the teachings presented in theforegoing descriptions and the associated drawings. Therefore, it is tobe understood that the inventions are not to be limited to the specificembodiments disclosed and that modifications and other embodiments areintended to be included within the scope of the appended claims.Moreover, although the foregoing descriptions and the associateddrawings describe example embodiments in the context of certain examplecombinations of elements and/or functions, it should be appreciated thatdifferent combinations of elements and/or functions may be provided byalternative embodiments without departing from the scope of the appendedclaims. In this regard, for example, different combinations of elementsand/or functions than those explicitly described above are alsocontemplated as may be set forth in some of the appended claims.Although specific terms are employed herein, they are used in a genericand descriptive sense only and not for purposes of limitation.

That which is claimed:
 1. A mapping system comprising: a memorycomprising map data; and processing circuitry configured to: receiveprobe data points associated with a plurality of vehicles, each probedata point received from a probe apparatus of a plurality of probeapparatuses, each probe apparatus comprising one or more sensors andbeing onboard a respective vehicle, wherein each probe data pointcomprises location information associated with the respective probeapparatus; for each of the probe data points, determine a location and aroad segment corresponding to the location; generate, from the probedata points associated with a first road segment, a probe densityhistogram for the first road segment, wherein the probe densityhistogram represents a volume of probe data points at each of aplurality of positions across a width of the road segment; identify,based on the probe density histogram, probe data points that aredetermined to be map-matched and classify the probe data pointsdetermined to be map-matched as map-matched probe data points; andidentify, based on the probe density histogram, probe data points thatare determined to be non-map-matched and classify the probe data pointsdetermined to be non-map-matched as non-map-matched probe data points;wherein probe data points classified as map-matched are analyzed withrespect to a position of a road segment of a digital map correspondingto the map-matched road segment; and wherein probe data pointsclassified as non-map-matched are analyzed to establish one or more oflane position, lane-based traffic flow, or road centerline accuracy. 2.The mapping system of claim 1, wherein the probe data points associatedwith a plurality of vehicles are received from a probe data provider,and wherein the probe data provider is classified according to whetherthe probe data provided is classified as map-matched, as non-mapmatched, or as a combination of map-matched and non-map-matched.
 3. Themapping system of claim 2, wherein in response to the probe dataprovider being classified as providing a combination of map-matched andnon-map-matched probe data, the processing circuitry being furtherconfigured to determine a proportion of the probe data points classifiedas map-matched and a proportion of the probe data points classified asnon-map-matched, and classifying the probe data provider according tothe proportions determined.
 4. The mapping system of claim 1, whereinthe probe data points are grouped into bins at each of the plurality ofpositions across the width of the road segment, wherein the processingcircuitry configured to identify probe data points that are determinedto be map-matched comprises causing the processing circuitry to:identify a relatively high proportion of probe data points grouped to atleast one of the plurality of positions across the width of the roadsegment, wherein the relatively high proportion of probe data points isestablished based on a statistically significant deviation at the atleast one of the plurality of positions across the width of the roadsegment relative to the probe density histogram adjacent the at leastone of the plurality of positions; and identify the probe data pointsgrouped to the at least one of the plurality of positions across thewidth of the road segment as map-matched and classify the probe datapoints grouped to the at least one of the plurality of positions asmap-matched probe data points.
 5. The mapping system of claim 1, furthercomprising causing the processing circuitry to: identify a peak in theprobe density histogram; assign probe data points at a position acrossthe width of the road segment corresponding to the peak in the probedensity histogram a peak value; assign probe data points at positionsalong the width of the road segment values corresponding to theirposition relative to the peak in the probe density histogram, wherevalues assigned are inversely proportional to a distance of the positionacross the width of the road segment from the peak; and wherein causingthe processing circuitry to identify, based on the probe densityhistogram, probe data points that are determined to be map-matched andclassify the probe data points determined to be map-matched asmap-matched probe data points comprises identifying probe data pointshaving a value satisfying a predetermined threshold as map-matched probedata points.
 6. The mapping system of claim 1, wherein the processingcircuitry is further configured to: calculate, for probe data pointsfrom each probe apparatus, a probability that the respective probe datapoints are map-matched based on a proportion of the probe data pointsfrom the respective probe apparatus corresponding to a peak in the probedensity histogram, wherein a peak in the probe density histogramcomprises a relatively high proportion of probe data points grouped toat least one of the plurality of positions across the width of the roadsegment, wherein the relatively high proportion of probe data points isestablished based on a statistically significant deviation at the atleast one of the plurality of positions across the width of the roadsegment relative to the probe density histogram adjacent the at leastone of the plurality of positions.
 7. The mapping system of claim 6,wherein the processing circuitry is further configured to: determine aconfidence value of the probability for probe data points from eachprobe apparatus, wherein the confidence value is determined based on anumber of probe data points from a respective probe apparatus, whereinthe confidence value of the probability is proportional to a number ofprobe data points from the respective probe apparatus.
 8. The mappingsystem of claim 7, wherein the probe data points from a respective probeapparatus are identified as map-matched in response to the probabilitythat the respective probe data points are map-matched satisfying apredetermined probability and the confidence value of the probabilityfor the probe data points from the respective probe apparatus satisfyinga predetermined confidence value.
 9. An apparatus comprising at leastone processor and at least one memory including computer program code,the at least one memory and computer program code configured to, withthe processor, cause the apparatus to at least: receive probe datapoints associated with a plurality of vehicles, each probe data pointreceived from a probe apparatus of a plurality of probe apparatuses,each probe apparatus comprising one or more sensors and being onboard arespective vehicle, wherein each probe data point comprises locationinformation associated with the respective probe apparatus; for each ofthe probe data points, determine a location and a road segmentcorresponding to the location; generate, from the probe data pointsassociated with a first road segment, a probe density histogram for thefirst road segment, wherein the probe density histogram represents avolume of probe data points at each of a plurality of positions across awidth of the road segment; identify, based on the probe densityhistogram, probe data points that are determined to be map-matched andclassify the probe data points determined to be map-matched asmap-matched probe data points; and identify, based on the probe densityhistogram, probe data points that are determined to be non-map-matchedand classify the probe data points determined to be non-map-matched asnon-map-matched probe data points; wherein probe data points classifiedas map-matched are analyzed with respect to a position of a road segmentof a digital map corresponding to the map-matched road segment; andwherein probe data points classified as non-map-matched are analyzed toestablish one or more of lane position, lane-based traffic flow, or roadcenterline accuracy.
 10. The apparatus of claim 9, wherein the probedata points associated with a plurality of vehicles are received from aprobe data provider, and wherein the probe data provider is classifiedaccording to whether the probe data provided is classified asmap-matched, as non-map matched, or as a combination of map-matched andnon-map-matched.
 11. The apparatus of claim 10, wherein in response tothe probe data provider being classified as providing a combination ofmap-matched and non-map-matched probe data, determining a proportion ofthe probe data points classified as map-matched and a proportion of theprobe data points classified as non-map-matched, and classifying theprobe data provider according to the proportions determined.
 12. Theapparatus of claim 9, wherein the probe data points are grouped intobins at each of the plurality of positions across the width of the roadsegment, wherein the apparatus caused to identify probe data points thatare determined to be map-matched comprises causing the apparatus to:identify a relatively high proportion of probe data points grouped to atleast one of the plurality of positions across the width of the roadsegment, wherein the relatively high proportion of probe data points isestablished based on a statistically significant deviation at the atleast one of the plurality of positions across the width of the roadsegment relative to the probe density histogram adjacent the at leastone of the plurality of positions; and identify the probe data pointsgrouped to the at least one of the plurality of positions across thewidth of the road segment as map-matched and classify the probe datapoints grouped to the at least one of the plurality of positions asmap-matched probe data points.
 13. The apparatus of claim 9, furthercomprising causing the apparatus to: identify a peak in the probedensity histogram; assign probe data points at a position across thewidth of the road segment corresponding to the peak in the probe densityhistogram a peak value; assign probe data points at positions along thewidth of the road segment values corresponding to their positionrelative to the peak in the probe density histogram, where valuesassigned are inversely proportional to a distance of the position acrossthe width of the road segment from the peak; and wherein causing theprocessing circuitry to identify, based on the probe density histogram,probe data points that are determined to be map-matched and classify theprobe data points determined to be map-matched as map-matched probe datapoints comprises identifying probe data points having a value satisfyinga predetermined threshold as map-matched probe data points.
 14. Theapparatus of claim 9, wherein the apparatus is further caused to:calculate, for probe data points from each probe apparatus, aprobability that the respective probe data points are map-matched basedon a proportion of the probe data points from the respective probeapparatus corresponding to a peak in the probe density histogram,wherein a peak in the probe density histogram comprises a relativelyhigh proportion of probe data points grouped to at least one of theplurality of positions across the width of the road segment, wherein therelatively high proportion of probe data points is established based ona statistically significant deviation at the at least one of theplurality of positions across the width of the road segment relative tothe probe density histogram adjacent the at least one of the pluralityof positions.
 15. The apparatus of claim 14, wherein the apparatus isfurther caused to: determine a confidence value of the probability forprobe data points from each probe apparatus, wherein the confidencevalue is determined based on a number of probe data points from arespective probe apparatus, wherein the confidence value of theprobability is proportional to the number of probe data points from therespective probe apparatus.
 16. The apparatus of claim 15, wherein theprobe data points from a respective probe apparatus are identified asmap-matched in response to the probability that the respective probedata points are map-matched satisfying a first predetermined probabilityand the confidence value of the probability for the probe data pointsfrom the respective probe apparatus satisfying a predeterminedconfidence value.
 17. A computer program product comprising at least onenon-transitory computer-readable storage medium havingcomputer-executable program code portions stored therein, thecomputer-executable program code portions comprising program codeinstructions configured to: receive probe data points associated with aplurality of vehicles, each probe data point received from a probeapparatus of a plurality of probe apparatuses, the probe apparatuscomprising a plurality of sensors and being onboard a respectivevehicle, wherein each probe data point comprises location informationassociated with the respective probe apparatus; for each of the probedata points, determine a location and a road segment corresponding tothe location; generate, from the probe data points associated with afirst road segment, a probe density histogram for the first roadsegment, wherein the probe density histogram represents a volume ofprobe data points at each of a plurality of positions across a width ofthe road segment; identify, based on the probe density histogram, probedata points that are determined to be map-matched and classify the probedata points determined to be map-matched as map-matched probe datapoints; and identify, based on the probe density histogram, probe datapoints that are determined to be non-map-matched and classify the probedata points determined to be non-map-matched as non-map-matched probedata points; wherein probe data points classified as map-matched areanalyzed with respect to a position of a road segment of a digital mapcorresponding to the map-matched road segment; and wherein probe datapoints classified as non-map-matched are analyzed to establish one ormore of lane position, lane-based traffic flow, or road centerlineaccuracy.
 18. The computer program product claim 17, wherein the probedata points associated with a plurality of vehicles are received from aprobe data provider, and wherein the probe data provider is classifiedaccording to whether the probe data provided is classified asmap-matched, as non-map matched, or as a combination of map-matched andnon-map-matched.
 19. The computer program product of claim 18, whereinin response to the probe data provider being classified as providing acombination of map-matched and non-map-matched probe data, determining aproportion of the probe data points classified as map-matched and aproportion of the probe data points classified as non-map-matched, andclassifying the probe data provider according to the proportionsdetermined.
 20. The computer program product of claim 17, wherein theprobe data points are grouped into bins at each of the plurality ofpositions across the width of the road segment, wherein the processingcircuitry configured to identify probe data points that are determinedto be map-matched comprises program code instructions configured to:identify a relatively high proportion of probe data points grouped to atleast one of the plurality of positions across the width of the roadsegment, wherein the relatively high proportion of probe data points isestablished based on a statistically significant deviation at the atleast one of the plurality of positions across the width of the roadsegment relative to the probe density histogram adjacent the at leastone of the plurality of positions; and identify the probe data pointsgrouped to the at least one of the plurality of positions across thewidth of the road segment as map-matched and classify the probe datapoints grouped to the at least one of the plurality of positions asmap-matched probe data points.